Top Data Engineer Jobs in Warsaw

Lead the architecture and development of scalable data pipelines, production datasets, and cloud data infrastructure. Manage schema evolution, backfills, performance optimization, incident response, observability, and reliable customer-facing reporting. Partner with Data Science, Analytics, and Product teams while mentoring engineers and establishing best practices for code quality, testing, security, and system design.

Build and optimize data models, integrate data from databases, APIs, streaming platforms, and files, and maintain reliable, cost-efficient pipelines and storage. The role involves troubleshooting data quality issues, reducing technical debt, and partnering with business stakeholders to translate requirements into effective data solutions. Strong SQL, Python, cloud platform knowledge, and fluency in Polish and English are required. The position is based in Warsaw with three office days per week.

Designs and builds production-grade AI applications natively on Snowflake using Cortex Analyst, Cortex Search, Snowflake Agents, semantic models, Python, and SQL. Develops secure, performant natural-language querying, retrieval, text-to-SQL, and agentic workflows over governed data. Owns delivery of a technical workstream, collaborates with data scientists, engineers, clients, and stakeholders, and supports enterprise GenAI and analytics solutions. Snowflake implementation experience is mandatory; regulated-industry experience is advantageous.

Designs, builds, and operates ING’s resilient data analytics fallback platform. Responsibilities include developing Spark, Airflow, Iceberg, Trino, and object-storage solutions; implementing data replication, synchronization, backup, recovery, and disaster-recovery capabilities; improving observability and operational resilience; and supporting critical analytical workloads. The role also contributes to architecture, automation, technical standards, cross-team collaboration, and on-call operations.

Designs and operates Planet’s modern data platform using Snowflake, Fivetran, and dbt. Responsibilities include building scalable ETL/ELT and reverse ETL pipelines, dimensional models, data products, quality and observability frameworks, governance practices, and near-real-time architectures. The role leads architectural decisions, troubleshooting, documentation, compliance, and platform optimization while mentoring data engineers and partnering with analysts, AI engineers, and business stakeholders.

Designs and operates production machine learning systems for predictive customer lifetime value. Responsibilities include feature engineering, gradient-boosted and probabilistic modeling, calibration, identity resolution, model monitoring, drift detection, data pipelines, cloud-based ML workflows, API integrations, and software engineering practices such as testing, CI/CD, and deployment safeguards.

Build and operate production-grade Kafka-compatible streaming systems and Apache Flink applications. Develop Go services, manage event processing, state, checkpoints, schemas, and delivery guarantees, and deploy workloads on AWS and Kubernetes. Troubleshoot distributed production systems while improving reliability, scalability, performance, and resilience. Collaborate with engineers on platform evolution and apply AI-powered development tools and GenAI workflows.

Build scalable, production-grade data pipelines and DBT models for large curated AI training datasets. Translate customer requirements into reusable data models, optimize queries across datasets of up to one billion rows, perform visual QA, and automate curation workflows. Partner with business development, data science, content, and engineering teams, manage technical implementation timelines, conduct code reviews, and improve testing and statistical rigor.

Own and operate a business-critical data platform, developing SQL databases, data models, integrations, pipelines, and automated data quality controls. Build trusted datasets and semantic layers for Power BI reporting, maintain documentation and lineage, manage access and versioning, troubleshoot failures, and collaborate with business and technical stakeholders. Continuously improve automation, governance, performance, and reporting accuracy across sustainability and operational data use cases.

Develops custom software and data platform solutions under team guidance. Completes assigned development tasks, troubleshoots straightforward technical issues, works with data integrations and pipelines, applies software engineering and Agile practices, and follows validation, quality, and regulatory requirements. The role emphasizes learning, collaboration, transparent communication, and growth across programming, databases, cloud services, DevOps, and data engineering.

Designs and operates batch and streaming data pipelines on Databricks and Spark for fintech lending use cases. Owns feature-store reliability, point-in-time correctness, data modeling, governance, observability, incident response, and data-quality controls. Develops agentic workflows with tools, evaluations, state, and guardrails around feature creation and monitoring. Requires strong Python, SQL, Spark, lakehouse, orchestration, and financial data engineering expertise.

Design and own the feature-store architecture supporting credit decisioning, including schemas, point-in-time correctness, freshness SLAs, lineage, data quality, observability, and drift monitoring. Build reference data-engineering patterns and lead complex cross-domain initiatives in regulated environments. Architect agentic harnesses and loop or graph workflows with reliable context, tools, evaluations, guardrails, and cost controls. Serve as a hands-on technical authority who establishes standards and mentors through influence.

Designs and maintains scalable data ingestion, transformation, ETL/ELT, and data quality pipelines using Databricks and cloud platforms. Builds metadata-driven frameworks, supports Bronze-to-Gold data flows, implements testing and reconciliation, and contributes to monitoring, CI/CD, migrations, performance optimization, and operational support. Collaborates with data, BI, platform, architecture, and client teams while supporting enterprise data products and knowledge transfer.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Design and optimize Snowflake data models and dbt transformation workflows, modernize Oracle ETL processes, build Python automation, and support scalable data architecture. The role involves troubleshooting data quality and performance issues, contributing to DevOps and deployment activities, owning technical deliverables, and participating in client-facing discussions and solution design.

Design, develop, and maintain scalable Databricks data pipelines and ETL/ELT workflows using Python and SQL. Optimize cloud data solutions for performance, reliability, and cost efficiency across Azure, AWS, or GCP. Build data models, tables, and integrations; monitor and troubleshoot pipelines; collaborate with analysts, scientists, and cross-functional teams; and support AI, analytics, and MLOps initiatives.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs and maintains CI/CD pipelines, reusable delivery templates, deployment automation, artifact distribution, Docker workflows, and Kubernetes environments. Automates platform engineering activities, supports Python packaging migrations from Poetry or setuptools to UV, and improves software delivery governance, traceability, and repeatability. The role also collaborates with engineering teams on DevOps practices and maintains technical documentation, with opportunities to support GCP-based enterprise data platforms.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and maintain high-performance data lakehouse infrastructure using Java and Rust. Develop reliable Apache Iceberg maintenance workflows, including compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Ensure data integrity under concurrency, retries, and failures. Maintain a Rust-based SQL query router, develop connectors, review open-source contributions, support production deployments, and participate in on-call debugging.

Design, implement, and maintain scalable streaming and batch data pipelines using Apache Pinot, Iceberg, Flink, and Spark. Improve pipeline performance, reliability, observability, and data quality while participating in software development, testing, incident resolution, and postmortems. Collaborate with engineering, product, and data teams, use AI coding tools, and continuously develop expertise in production data systems.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Own and evolve a production lakehouse supporting millions of daily financial events. Design bronze, silver, and gold layers; build CDC streaming pipelines; optimize storage and queries; manage retention, archival, reconciliation, and data correctness. Establish governance through cataloging, lineage, access controls, masking, encryption, and audit trails. Monitor ingestion health, anomalies, and cloud costs while supporting reliable, traceable data for Analytics, Finance, and Regulatory teams.